LeetCode || Number of 1 Bits

本文探讨了在处理32位无符号整数时,计算HAMMING权值的三种不同方法,并分析了每种方法的时间效率及对负数情况的处理策略。通过比较,展示了如何降低循环次数以提高计算速度。

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解法1: Accepted

耗时4ms。

但是没有考虑负数的情况,本题也没有测试这种情况。


class Solution {
public:
    int hammingWeight(uint32_t n) {
        int count = 0;
        while(n!=0)
        {
            if(n&1)count++;
            n= n >>1;
        }
        return count;
    }
};


解法2: Accepted

耗时4ms。

考虑了出现负数的情况,解决了陷入死循环的问题。

int hammingWeight(uint32_t n) {
	uint32_t tmp = 1;
	int count= 0;
	while(tmp)//会执行到tmp溢出结束
	{
		if(n & tmp)
			count++;
		tmp = tmp << 1;
	}
	return count;
}

解法3: Accepted

耗时4ms。

减低了while的循环次数。

class Solution {
public:
    int hammingWeight(uint32_t n) {
        int count= 0;
        while(n)
        {
            count++;
            n = n &(n-1);//相当于每次消灭一个1
        }
        return count;
    }
};



### LeetCode Problems Involving Counting the Number of 1s in Binary Representation #### Problem Description from LeetCode 191. Number of 1 Bits A task involves writing a function that receives an unsigned integer and returns the quantity of '1' bits within its binary form. The focus lies on identifying and tallying these specific bit values present in any given input number[^1]. ```python class Solution: def hammingWeight(self, n: int) -> int: count = 0 while n: count += n & 1 n >>= 1 return count ``` This Python code snippet demonstrates how to implement the solution using bitwise operations. #### Problem Description from LeetCode 338. Counting Bits Another related challenge requires generating an output list where each element represents the amount of set bits ('1') found in the binary notation for integers ranging from `0` up to a specified value `n`. This problem emphasizes creating an efficient algorithm capable of handling ranges efficiently[^4]. ```python def countBits(num): result = [0] * (num + 1) for i in range(1, num + 1): result[i] = result[i >> 1] + (i & 1) return result ``` Here, dynamic programming principles are applied alongside bitwise shifts (`>>`) and AND (`&`) operators to optimize performance during computation. #### Explanation Using Brian Kernighan Algorithm For optimizing further especially with large inputs, applying algorithms like **Brian Kernighan** offers significant advantages due to reduced iterations needed per operation compared against straightforward methods iterating through all possible positions or dividing repeatedly until reaching zero. The core idea behind this method relies upon subtracting powers-of-two corresponding only to those places holding actual ‘ones’ thereby skipping over zeroes entirely thus reducing unnecessary checks: ```python def hammingWeight(n): count = 0 while n != 0: n &= (n - 1) count += 1 return count ``` --related questions-- 1. How does the Hamming weight calculation differ between signed versus unsigned integers? 2. Can you explain why shifting right works effectively when determining counts of one-bits? 3. What optimizations exist beyond basic iteration techniques for calculating bit counts? 4. Is there any difference in implementation logic required across various programming languages supporting similar syntaxes? 5. Why might someone choose the Brian Kernighan approach over other strategies?
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